Guide to Statistics and Medicine

This JAMA Guide to Statistics and Methods explains worst-rank score methods, a nonparametric statistical technique that assigns worst-case outcomes for patients with missing data to account for missingness that may reflect an adverse change in patient status (informative rather than random missingness).

This JAMA Guide to Statistics and Methods explains the differences between risk ratios and odds ratios and when each is the more appropriate statistic to estimate measures of effect or association in research findings.

This JAMA Guide to Statistics and Methods summarizes latent class analysis, a statistical technique that estimates the probability of patients belonging to a discrete group that shares specific combinations of observed variables, and explains how the technique is used and can be interpreted in observational research.

This JAMA Guide to Statistics and Methods explains the use of regression discontinuity analysis on observational data—the difference in effect estimate between regression analyses using an exposure variable above and beneath a threshold of interest—to distinguish changes attributable to an intervention from background ecological or secular changes.

This JAMA Guide to Statistics and Methods reviews the use of prerandomization run-in periods to improve treatment adherence and reduce loss to follow-up, and explains how they should be interpreted.

This JAMA Guide to Statistics and Methods reviews the susceptible-infected-recovered (SIR) model for predicting the course of infectious disease outbreaks, which describes the transition of individuals from susceptible to infected and from infected to recovered, and discusses the model’s limitations, including oversimplification of complex disease processes.

This JAMA Guide to Statistics and Methods reviews overlap weighting, a technique to reduce the influence of patients who are nearly always treated or never treated on propensity score estimates, when attempting to reduce bias associated with nonrandomized treatment in observational study populations.

This JAMA Guide to Statistics and Methods reviews common types of nonparametric statistics, which make no assumptions about underlying population distribution, and explains when they are appropriate to use.

This JAMA Guide to Statistics and Methods explains the meaning underlying the proportional hazards (PH) assumption underlying Cox regression and survival analyses, and proposes that reports of survival differences might replace statistical tests of the PH assumption because they are more meaningful.

This JAMA Guide to Statistics and Methods reviews how and under what conditions surrogate outcomes can replace patient-centered outcomes in randomized trials and stresses the importance of properly validating outcomes as surrogates for direct measures of patient experiences.

This JAMA Guide to Statistics and Methods reviews how health state utility assessment can be used to calculate quality-adjusted life-years, a patient-specific measure of preference for health outcomes that incorporates quantity and quality of life.

This JAMA Guide to Statistics and Methods reviews how propensity score methods can be used with observational data to mimic comparison populations in a randomized trial and account for differences that might lead to biased conclusions.

This JAMA Guide to Statistics and Methods reviews the use of whole genome association studies to quantify the association between single-nucleotide polymorphisms (SNPs) and human disease, and the importance of using the information to identify the actual effector transcripts responsible for the underlying pathophysiology.

This JAMA Guide to Statistics and Methods reviews the use of instrumental variable analysis in observational and randomized studies and how, under specific assumptions, they can provide unbiased estimates of treatment effects even if unobserved confounding exists.